Extrem(ist +) Fightin’ Words

practicing

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load libraries

### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html
library(udpipe)

ud_model <- udpipe_download_model(language = "english")

library(tidyverse)

library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)

library(quanteda)

library(lattice)
library(latticeExtra)

library(plotly)

library(pdp)

library(patchwork)

load the FW functions

### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#

fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  #rankplot <- gather(wideplotmat, party, zetarank, 1:ncol(zetarankmat))
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F, max.overlaps = Inf) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
#    labs(x="Terms used more frequently overall →", y="Terms used more frequently by group →",  title=title, subtitle=subtitle , caption = caption) 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

options(ggrepel.max.overlaps = Inf)

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}

Compare Associated Press 1994-2010: Before and After “extremist” and other query terms

Query search: (activi* | ahbash | akromiya | anjem | ansharut | anticapital* | antidemocr* | antiestablish* | antifa | antigovern* | antimilitar* | antimonarch* | antipatri* | antireli* | antisem* | antisocia* | antisyst* | apost* | atharis | athei* | atheists | außerparlamentari* | authoritar* | bagau* | bigots | bplf | bukhari* | capitulatio* | conspirato* | counterj* | cybercalip* | damigo | dawro* | demon* | deradicaliza* | deviatio* | diqqi | dissid* | djamaat | dotbus* | ecofas* | espou* | ethnonationa* | extrem* | facists | fadaia* | fanat* | fasci* | fetö | fightdem* | freedo* | fundamental* | fuqra | gafatar | gamerga* | gemidzii | ghuluww | globali* | gramsc* | gülen* | hacktiv* | haquna | hardline | harkatul | hatemon* | heimwe* | hezbol* | hinduph* | hindutva | hizbut | hojja* | ideolog* | incitem* | inciters | insurr* | intacti* | islam4uk | islam* | jaljalat | jbakc | jihadi* | jmjb | jrtn | judai* | juhayman | jundu* | kadiza* | kahanism | kahanist | karram* | kaysa* | khalis* | khatmia | khawarij | khomein* | koutla | leftist | leftists | leftwing | liberatio* | madkha* | madkhal* | maimonid* | manosp* | mauras* | mcln | militan* | millatu | monarc* | mudja* | muhaji* | mujahid* | murab* | muttahi* | najjadah | nationali* | neofas* | neona* | opantish | oppositi* | paleolibertar* | paramili* | parliamenta* | pegida | populist | principa* | profe* | prowar | putinist | qadari | quranism | quranist | qutbism | qutbist | qutbists | racis* | radica* | reactioni* | reformis* | reichsbürgerbewe* | rightist | rightw* | rofiq | russoph* | sabireen | sadda* | salaf* | sayaff | scriptura* | secula* | separationi* | sharia4hol* | sikrikim | split* | squadism | strasse* | subver* | suidlan* | sukarn* | suprema* | supremac* | sympathi* | table* | tabliq | takfir | takfir* | takfi* |terror* | theoc* | titoite | triba* | trots* | trotsk* | ukrainoph* | ultraconserva* | ultralib* | ultranationa* | ultrar* | uscmo | wahabbi | wahab* | wahha* | xenoph* | yulde* | zinovie*)

Load and clean the data

• to string & lower text
• pivot to long format
• apply text_cleaner to one column “context.text”

text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

Calculate FW.

e <- dfm(extremecorpus$content)
message(dim(e))
head(e)

Document-feature matrix of: 6 documents, 14,891 features (100.0% sparse).
       features
docs    us struggl uptick american plot attack past month last week
  text1  1       1      1        1    1      0    0     0    0    0
  text2  0       0      0        0    0      1    1     1    1    1
  text3  0       0      0        0    0      0    0     0    0    0
  text4  0       0      0        0    0      0    0     0    0    0
  text5  0       0      0        0    0      0    0     0    0    0
  text6  0       0      0        0    0      0    0     0    0    0
[ reached max_nfeat ... 14,881 more features ]

#############################################

e <- dfm_select(e, pattern = stopwords("english"), selection = "remove")
e <- dfm_select(e, min_nchar = 2)
e <- dfm_trim(e, min_termfreq = 4, min_docfreq = .05, verbose=TRUE)

#dim(e)
# sparsity(e)

#############################################

extrem_dtm <- convert(e, to='data.frame')
extrem_dtm <- extrem_dtm[-c(1)]
w <- which( sapply(extrem_dtm, class ) == 'character' )

#############################################

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_NYT.dfm.long$Context)

Get and show the top words per group by zeta.

Context.before	Context.after
islam	group
palestinian	hussein
war	attack
iraqi	parti
suspect	elect
crack	leader
jemaah	jihad
crackdown	milit
main	movement
alleg	said
support	guerrilla
evid	network
albanian	regim
prodemocraci	armi
sept	politician
muslim	organ
megawati	outsid
battl	univers
prevent	bomb
kosovo	rocket

Plot: Before in Blue, After in Red

Calculate by query item/search term

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit
extrem_dtm_topn$Context <- extrem_NYT.dfm.long$Context

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep[grep("before",extrem_dtm_topn_keep$Context),]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep[grep("after", extrem_dtm_topn_keep$Context),]

rr <-dim(topn_terms)[1]
r <- sum(length(extrem_dtm_topn_keep_before))
topn_terms_before <- topn_terms[seq(1,rr,2),]
topn_terms_after <- topn_terms[seq(2,rr,2),]

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep_before[-c(1, r-1, r)]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep_after[-c(1, r-1, r)]

rm(rr)
rm(r)
rm(extrem_dtm_topn)

#############################################
fw.query_item_before <- fwgroups(extrem_dtm_topn_keep_before,groups = topn_terms_before$Query.item)
fwkeys.query_item_before <- fw.keys(fw.query_item_before, n.keys=15)
kable(fwkeys.query_item_before, caption = "Top 15 Words for Query Term: BEFORE")

Top 15 Words for Query Term: BEFORE

Islamic	militants	opposition	Saddam	terrorist
milit	islam	main	iraqi	sept
organ	palestinian	elect	presid	alqaida
strict	kill	polit	iraq	bin
radic	suspect	parliament	war	laden
somalia	attack	opposit	saddam	involv
hama	taliban	parti	oust	unit
secular	isra	minist	baghdad	suspect
hardlin	muslim	vote	fall	link
iran	gaza	democrat	bush	state
turkey	armi	prime	hussein	consid
extremist	troop	strong	captur	connect
malaysia	israel	percent	regim	appar
suprem	pakistan	despit	usl	osama
gaza	forc	poll	former	charg
fundamentalist	arafat	voic	death	list

p.fw.query_item_before <- fw.ggplot.groups(fw.query_item_before,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('blue','blue','blue', 'blue','blue'),
                                           title = 'Comparison of Terms by Overall Top Terms: BEFORE')
p.fw.query_item_before

#############################################
fw.query_item_after <- fwgroups(extrem_dtm_topn_keep_after,groups = topn_terms_after$Query.item)
fwkeys.query_item_after <- fw.keys(fw.query_item_after, n.keys=15)
kable(fwkeys.query_item_after, caption = "Top 15 Words for Query Term: AFTER")

Top 15 Words for Query Term: AFTER

Islamic	militants	opposition	Saddam	terrorist
milit	isra	parti	hussein	attack
group	israel	leader	regim	organ
jihad	kashmir	democrat	iraq	state
hama	gaza	vote	iraqi	unit
movement	palestinian	parliament	un	activ
law	kill	lawmak	weapon	group
insurg	attack	protest	saddam	act
extremist	region	elect	son	network
front	southern	candid	captur	bomb
revolut	fire	politician	kuwait	suspect
republ	rocket	coalit	mass	threat
court	pakistani	social	baghdad	financ
fundamentalist	fight	conserv	us	sept
news	border	win	resolut	link
agenc	area	opposit	palac	cell

p.fw.query_item_after <- fw.ggplot.groups(fw.query_item_after,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('red', 'red','red','red','red'),
                                          title = 'Comparison of Terms by Overall Top Terms: AFTER')
p.fw.query_item_after

NA
NA

Calculate Parts of speech by before and after

Calculate FW and keys

ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

A few barchart functions

## UNIVERSAL PoS
UPOS_barchart <- function(df1, df2){
  stats1 <- txt_freq(df1$upos)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- txt_freq(df2$upos)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = stats1, col = "cadetblue", 
        main = "UPOS (Universal Parts of Speech)\n frequency of occurrence: BEFORE vs AFTER", 
         xlab = "Freq"), 
    barchart(key ~ freq, data = stats2, col =  'skyblue',
         xlab = "Freq"))
}



## NOUNS
NOUNS_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("NOUN")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("NOUN")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring nouns: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
            xlab = "Freq"))
}

## ADJECTIVES
ADJ_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("ADJ")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("ADJ")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring adjectives: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
         xlab = "Freq"))
}

## Using RAKE to find keywords
RAKE_KW_barchart <- function(df1,df2){
  
  stats1 <- keywords_rake(x = df1, term = "lemma", group = "doc_id", 
                         relevant = df1$upos %in% c("NOUN", "ADJ"))
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  stats2 <- keywords_rake(x = df2, term = "lemma", group = "doc_id", 
                         relevant = df2$upos %in% c("NOUN", "ADJ"))
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  
  c(barchart(key ~ rake, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by RAKE: BEFORE vs AFTER", 
           xlab = "Rake"),
    barchart(key ~ rake, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "Rake"))
}

## Using Pointwise Mutual Information Collocations
PWI_barchart <- function(df1, df2){
  
  df1$word <- tolower(df1$token)
  stats1 <- keywords_collocation(x = df1, term = "word", group = "doc_id")
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$word <- tolower(df2$token)
  stats2 <- keywords_collocation(x = df2, term = "word", group = "doc_id")
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ pmi, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by PMI Collocation: BEFORE vs AFTER", 
           xlab = "PMI (Pointwise Mutual Information)"),
      barchart(key ~ pmi, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "PMI (Pointwise Mutual Information)"))
}

## Using a sequence of POS tags (noun phrases / verb phrases)
POS_barchart <- function(df1, df2){
  
  df1$phrase_tag <- as_phrasemachine(df1$upos, type = "upos")
  stats1 <- keywords_phrases(x = df1$phrase_tag, term = tolower(df1$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats1 <- subset(stats1, ngram > 1 & freq > 3)
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$phrase_tag <- as_phrasemachine(df2$upos, type = "upos")
  stats2 <- keywords_phrases(x = df2$phrase_tag, term = tolower(df2$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats2 <- subset(stats2, ngram > 1 & freq > 3)
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Keywords - simple noun phrases: BEFORE vs AFTER", xlab = "Frequency"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
               xlab = "Frequency"))
}

Bar Charts from Functions Above

UPOS_barchart(x_odd.before, x_even.after)

NOUNS_barchart(x_odd.before, x_even.after)

ADJ_barchart(x_odd.before, x_even.after)

RAKE_KW_barchart(x_odd.before, x_even.after)

PWI_barchart(x_odd.before, x_even.after)

POS_barchart(x_odd.before, x_even.after)

Cooccurences

CO_OC_noun_adj_same_sent.before <- function(df1){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df1, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: BEFORE", subtitle = "Nouns & Adjective")
  
}

CO_OC_noun_adj_same_sent.after <- function(df2){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df2, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: AFTER", subtitle = "Nouns & Adjective")
  
}


CO_OC_noun_adj_same_sent.before(x_odd.before)

Attaching package: ‘igraph’

The following object is masked from ‘package:plotly’:

    groups

The following object is masked from ‘package:quanteda’:

    as.igraph

The following objects are masked from ‘package:dplyr’:

    as_data_frame, groups, union

The following objects are masked from ‘package:purrr’:

    compose, simplify

The following object is masked from ‘package:tidyr’:

    crossing

The following object is masked from ‘package:tibble’:

    as_data_frame

The following objects are masked from ‘package:stats’:

    decompose, spectrum

The following object is masked from ‘package:base’:

    union

CO_OC_noun_adj_same_sent.after(x_even.after)

##########################################

CO_OC_noun_adj_following.before <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: BEFORE", subtitle = "Nouns & Adjective")
}

CO_OC_noun_adj_following.after <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "skyblue") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: AFTER", subtitle = "Nouns & Adjective")
}


CO_OC_noun_adj_following.before(x_odd.before)

CO_OC_noun_adj_following.after(x_even.after)

Cooccurences (part 2)

Corrs <- function(df){
  df$id <- unique_identifier(df, fields = c("sentence_id", "doc_id"))
  dtm <- subset(df, upos %in% c("NOUN", "ADJ"))
  dtm <- document_term_frequencies(dtm, document = "id", term = "lemma")
  dtm <- document_term_matrix(dtm)
  dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)
  termcorrelations <- dtm_cor(dtm)
  y <- as_cooccurrence(termcorrelations)
  y <- subset(y, term1 < term2 & abs(cooc) > 0.2)
  y <- y[order(abs(y$cooc), decreasing = TRUE), ]
  print(y[1:25,])
}


Corrs(x_odd.before)

Corrs(x_even.after)

Corrs(x_odd.before)

Corrs(x_even.after)

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